A Neural Network Modeling of Stress Behavior in Nonlinear Magnetostrictive Materials

Nouicer, Abdelmadjid; Nouicer, E.; Mahtali, M.; Féliachi, Mouloud

doi:10.1007/s10948-012-1990-6

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…The ve parameters can obtain through the experiment curve l-H using parameters identication. 14 In this paper, an experimental system of the GMA is built and is shown in Fig. 3.…”

Section: Hysteresis Model and Its Parameters Identicationmentioning

confidence: 99%

Hysteresis nonlinearity modeling and position control for a precision positioning stage based on a giant magnetostrictive actuator

Wang

Deng

et al. 2016

RSC Adv.

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A precision positioning stage based on giant magnetostrictive actuator (PPS-GMA) shows nonlinear displacement when it is used in the field of precision positioning control. To improve the defect, the Jiles-Atherton hysteresis model and the dynamic recurrent neural network (DRNN) feed forward-fuzzy PID feedback control strategy were adopted. An accurate hysteresis nonlinearity model of PPS-GMA was established with the Jiles-Atherton model and its parameters were identified using the particle swarm optimization (PSO) algorithm. A dynamics inverse model of the PPS-GMA was established with the DRNN learning method to compensate the hysteresis nonlinearity characteristic. A fuzzy PID feedback control was used to compensate for the mapping error of DRNN. Using these control methods, the positioning accuracy of the precision positioning stage was improved. The simulation and experimental results show that the Jiles-Atherton hysteresis model can describe the hysteresis nonlinear characteristic of the precision positioning stage, the PSO algorithm has high precision for parameter identification, the DRNN feed forward-fuzzy PID feedback control strategy can effectively eliminate the nonlinear characteristics of the PPS-GMA, which has practical significance for improving the positioning accuracy of the PPS-GMA.

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“…The ve parameters can obtain through the experiment curve l-H using parameters identication. 14 In this paper, an experimental system of the GMA is built and is shown in Fig. 3.…”

Section: Hysteresis Model and Its Parameters Identicationmentioning

confidence: 99%

Hysteresis nonlinearity modeling and position control for a precision positioning stage based on a giant magnetostrictive actuator

Wang

Deng

et al. 2016

RSC Adv.

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show abstract

“…Some approaches on utilizing ANNs in modeling magnetic media have been previously reported [29–36] . Nafalski et al [37] suggested using ANN as an entire substitute to hysteresis models.…”

Section: Utilizing Neural Network In Modeling Complex Magnetic Mediamentioning

confidence: 99%

Utilizing neural networks in magnetic media modeling and field computation: A review

Adly

Abd-El-Hafiz

2014

Journal of Advanced Research

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Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.

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Hysteresis online identification approach for smart material actuators with different input signals and external disturbances

Zhang

et al. 2022

Nonlinear Dyn

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A Neural Network Modeling of Stress Behavior in Nonlinear Magnetostrictive Materials

Cited by 8 publications

References 11 publications

Hysteresis nonlinearity modeling and position control for a precision positioning stage based on a giant magnetostrictive actuator

Hysteresis nonlinearity modeling and position control for a precision positioning stage based on a giant magnetostrictive actuator

Utilizing neural networks in magnetic media modeling and field computation: A review

Hysteresis online identification approach for smart material actuators with different input signals and external disturbances

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